Integrating modelling and smart sensors for environmental and human health

Stefan Reis, Edmund Seto, Amanda Northcross, Nigel W.T. Quinn, Matteo Convertino, Rod L. Jones, Holger R. Maier, Uwe Schlink, Susanne Steinle, Massimo Vieno, Michael C. Wimberly

Research output: Contribution to journalArticlepeer-review

83 Scopus citations


Sensors are becoming ubiquitous in everyday life, generating data at an unprecedented rate and scale. However, models that assess impacts of human activities on environmental and human health, have typically been developed in contexts where data scarcity is the norm. Models are essential tools to understand processes, identify relationships, associations and causality, formalize stakeholder mental models, and to quantify the effects of prevention and interventions. They can help to explain data, as well as inform the deployment and location of sensors by identifying hotspots and areas of interest where data collection may achieve the best results. We identify a paradigm shift in how the integration of models and sensors can contribute to harnessing 'Big Data' and, more importantly, make the vital step from 'Big Data' to 'Big Information'. In this paper, we illustrate current developments and identify key research needs using human and environmental health challenges as an example.

Original languageEnglish (US)
Pages (from-to)238-246
Number of pages9
JournalEnvironmental Modelling and Software
StatePublished - Dec 1 2015

Bibliographical note

Funding Information:
E.S. is funded by NIH R21ES024715 . M.C. gratefully acknowledges the Minnesota Discovery, Research and InnoVation Economy (MnDRIVE) “Global Food Venture” funding and the Institute on the Environment “Discovery Grant” funding at the University of Minnesota Twin-Cities. S.R. and S.S. acknowledge the support for the conceptual development and testing of personal exposure monitoring methods by the UK Natural Environment Research Council through National Capability funding.

Publisher Copyright:
© 2015 Published by Elsevier Ltd.


  • Big data
  • Environmental health
  • Environmental sensors
  • Integrated modelling
  • Population health


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